A Fundamental Overview of SOTA-Ensemble Learning Methods for Deep Learning: A Systematic Literature Review

Klaiber, Marco

doi:10.31763/sitech.v2i2.549

Cited by 5 publications

(3 citation statements)

References 45 publications

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“…It was developed in 1997 by Dopazo and Carazo [39]. SOTA combines hierarchical clustering and a Self-Organizing Map (SOM) based on a single-layer neural network [40]. In SOTA, the processing time is approximately directly proportional to the number of elements to be classified.…”

Section: Ai Classificationmentioning

confidence: 99%

AI Classification of Eggs’ Origin from Mycoplasma synoviae-Infected or Non-Infected Poultry via Analysis of the Spectral Response

Pakuła,

Paśko,

Marć

et al. 2023

Applied Sciences

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Rapid detection of Mycoplasma synoviae (MS) in a flock is crucial from the perspective of animals’ health and economic income. MS are highly contagious bacteria that can cause significant losses in commercial poultry populations when its prevalence is not limited. MS infections can cause losses associated with a range of clinical symptoms related to the respiratory, mobility and reproductive systems. Lesions related to the reproductive system and changes in the eggshell result in losses associated with infection or embryo death or complete destruction of the eggs. The authors propose using spectral measurements backed up by an AI data processing algorithm to classify eggs’ origin: from healthy hens or MS-infected ones. The newest obtained classification factors are F-scores for white eggshells of 99% and scores for brown eggshells of 99%—all data used for classification were obtained using a portable multispectral fibre-optics reflectometer. The proposed method may be used directly on the farm by staff members with limited qualifications, as well as by veterinary doctors, assistants, or customs officers. Moreover, this method is scalable to mass production of eggs. Standard methods such as serological tests require either specialized staff or laboratory equipment. Implementation of a non-destructive optical measurement method, which is easily adapted for use on a production line, is economically reasonable.

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Section: Ai Classificationmentioning

confidence: 99%

AI Classification of Eggs’ Origin from Mycoplasma synoviae-Infected or Non-Infected Poultry via Analysis of the Spectral Response

Pakuła,

Paśko,

Marć

et al. 2023

Applied Sciences

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“…[48,49] also consider applied SOTA issues; Ref. [50] provides a fundamental overview of SOTA-ensemble deep learning techniques.…”

Section: The Relevance Of Improving the Rul Forecasting Techniquesmentioning

confidence: 99%

Method for Forecasting the Remaining Useful Life of a Furnace Transformer Based on Online Monitoring Data

et al. 2023

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Implementing the concept of a “smart furnace transformer” should stipulate its information support throughout its life cycle. This requires improving techniques for estimating the transformer’s health and forecasting its remaining useful life (RUL). A brief review of the problem being solved has shown that the known RUL estimation techniques include processing the results of measuring the facility state parameters using various mathematical methods. Data processing techniques (deep learning, SOLA, etc.) are used, but there is no information on their application in online monitoring systems. Herewith, fast (shock) changes in the resource caused by the failures and subsequent recoveries of the facility’s health have not been considered. This reduces the RUL forecasting accuracy for the repairable equipment, including transformers. It is especially relevant to consider the impact of sudden state changes when it comes to furnace transformers due to a cumulative wear effect determined by their frequent connections to the grid (up to 100 times a day). The proposed approach is based on calculating the RUL by analytical dependencies, considering the failures and recoveries of the facility state. For the first time, an engineering RUL forecasting technique has been developed, based on the online diagnostic monitoring data results provided in the form of time series. The equipment’s relative failure tolerance index, calculated with analytical dependencies, has first been used in RUL forecasting. As a generalized indicator, a relative failure tolerance index considering the facility’s state change dynamics has been proposed. The application of the RUL forecasting technique based on the results of dissolved gas analysis of a ladle furnace unit’s transformer is demonstrated. The changes in the transformer state during the operation period from 2014 to 2022 have been studied. The RUL was calculated in the intensive aging interval; the winding dismantling results were demonstrated, which confirmed developing destructive processes in the insulation. The key practical result of the study is reducing accidents and increasing the service life of the arc and ladle furnace transformers. The techno-economic effect aims to ensure process continuity and increase the metallurgical enterprise’s output (we cannot quantify this effect since it depends on the performance of a particular enterprise). It is recommended to use the technique to forecast the RUL of repairable facilities equipped with online monitoring systems.

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“…In recent years, deep learning (DL) and machine learning (ML) have gained increasing attention, and their uses are constantly increasing in LC classification. DL is often confusing with ML, but it should be noted that DL is a subset of ML, and both belong to the category of artificial intelligence (AI) (Chen et al, 2019;Klaiber, 2021). The commonly used ML algorithms include linear regression, logistic regression, naïve bayes (NB), support vector machines (SVM), decision tree, Bayesian learning, K nearest neighbor (KNN), neural networks (NN) and random forest (RF) (Ray, 2019).…”

Section: Introductionmentioning

confidence: 99%

Optimal parameters of random forest for land cover classification with suitable data type and dataset on Google Earth Engine

Sun,

Ongsomwang

2023

Front. Earth Sci.

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Exact land cover (LC) map is essential information for understanding the development of human societies and studying the impacts of climate and environmental change. To fulfill this requirement, an optimal parameter of Random Forest (RF) for LC classification with suitable data type and dataset on Google Earth Engine (GEE) was investigated. The research objectives were 1) to examine optimum parameters of RF for LC classification at local scale 2) to classify LC data and assess accuracy in model area (Hefei City), 3) to identify a suitable data type and dataset for LC classification and 4) to validate optimum parameters of RF for LC classification with a suitable data type and dataset in test area (Nanjing City). This study suggests that the suitable data types for LC classification were Sentinel-2 data with auxiliary data. Meanwhile, the suitable dataset for LC classification was monthly and seasonal medians of Sentinel-2, elevation, and nighttime light data. The appropriate values of the number of trees, the variable per split, and the bag fraction for RF were 800, 22, and 0.9, respectively. The overall accuracy (OA) and Kappa index of LC in model area (Hefei City) with suitable dataset was 93.17% and 0.9102. In the meantime, the OA and Kappa index of LC in test area (Nanjing City) was 92.38% and 0.8914. Thus, the developed research methodology can be applied to update LC map where LC changes quickly occur.

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A Fundamental Overview of SOTA-Ensemble Learning Methods for Deep Learning: A Systematic Literature Review

Cited by 5 publications

References 45 publications

AI Classification of Eggs’ Origin from Mycoplasma synoviae-Infected or Non-Infected Poultry via Analysis of the Spectral Response

AI Classification of Eggs’ Origin from Mycoplasma synoviae-Infected or Non-Infected Poultry via Analysis of the Spectral Response

Method for Forecasting the Remaining Useful Life of a Furnace Transformer Based on Online Monitoring Data

Optimal parameters of random forest for land cover classification with suitable data type and dataset on Google Earth Engine

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